Medical diagnoses are often made after an examination of relevant images or other data on a clinical workstation (e.g., a personal computer, laptop, tablet, etc.). Such an examination is usually performed with the assistance of a computer based imaging application after images are obtained through an imaging scan (e.g., a PET scan, a MRI scan, etc.). For example, if a user of the clinical workstation (e.g., a physician, radiologist, etc.) would like to evaluate and diagnose a heart of a particular patient, the user opens the computer based application on a user interface (using, for example, a monitor other display), selects the patient's file to display images of the patient's heart obtained by the imaging scan, evaluates the images with the assistance of one or more application tools, and makes a diagnosis based on the evaluation. The time spent examining each case becomes an issue as the pressure on clinicians to examine more cases increases.
Conventional computer based applications attempt to address this issue with display protocols (DiPs) on a per-application basis. For example, DiPs allow clinicians to ensure that their medical workspace (i.e., the hanging protocol) looks the same way when they open a next case. In other words, conventional applications display a user interface (UI) layout before the user has seen the images undergoing diagnosis. Thus, the available diagnostic functionality of the application has to be decided by the user prior to the start of the application (e.g. in workflow mapping or ad-hoc task selection) without the user having seen the images, even though the proper choice of such functionality is important to diagnosis. The problem is that the applications, task flows, and preprocessing are not personalized, and no preserved user knowledge is available when a similar context re-appears. In other words, clinicians waste time selecting the proper diagnostic functionality of the application for each new case even if the new case requires similar diagnostic functionality as a previous case. Accordingly, clinicians wish to customize and personalize the applications to increase efficiency and performance.